Power profile management method and system

ABSTRACT

A power profile management method and system. The method includes retrieving by a computer processor input data associated with a user of power consumption devices at a specified location. The computer processor retrieves power consumption data comprising power consumption levels for the power consumption devices. The computer processor generates and transmits a mobile usage portfolio associated said user and the power consumption devices. The computer processor generates a load usage prediction report associated with the user and the power consumption devices. The load usage prediction report is generated based on the mobile usage portfolio and said power consumption data. The computer processor transmits the load usage prediction report to a power provider utility for analysis.

FIELD OF THE INVENTION

The present invention relates to a method and associated system formanaging a profile associated with power usage from a utility grid.

BACKGROUND OF THE INVENTION

Managing usage data from a power provider for various power consumptiondevices typically comprises an inaccurate process with littleflexibility. Usage of power provided by power providers typically variesdepending on conditions. Varying usage of power may cause powerproviders to modify output.

SUMMARY OF THE INVENTION

The present invention provides a power usage prediction methodcomprising:

retrieving, by a computer processor of a mobile computing system, firstinput data associated with a first user of a first plurality of powerconsumption devices at a first specified location;

retrieving, by said computer processor, first power consumption datacomprising a power consumption level for each power consumption deviceof said first plurality of power consumption devices;

generating, by said computer processor based on said first input data, afirst mobile usage portfolio associated said first user and said firstplurality of power consumption devices;

transmitting, by said computer processor to said first specifiedlocation, said first mobile usage portfolio;

generating, by said computer processor based on said first mobile usageportfolio and said first power consumption data, a first load usageprediction report associated with said first user and said firstplurality of power consumption devices; and

transmitting, by said computer processor to a power provider utility foranalysis, said first load usage prediction report.

The present invention provides a power usage prediction methodcomprising:

retrieving, by a computer processor of a computing system from a firstmobile computing system, first input data associated with a first userof a first plurality of power consumption devices at a first specifiedlocation;

retrieving, by said computer processor from a second mobile computingsystem, second input data associated with a second user of said firstplurality of power consumption devices at said first specified location;

retrieving, by said computer processor from said first location, firstpower consumption data comprising a power consumption level for eachpower consumption device of said first plurality of power consumptiondevices;

generating, by said computer processor based on said first input data, afirst mobile usage portfolio associated said first user and said firstplurality of power consumption devices;

transmitting, by said computer processor to said first specifiedlocation and said first mobile computing system, said first mobile usageportfolio;

generating, by said computer processor based on said second input data,a second mobile usage portfolio associated said second user and saidfirst plurality of power consumption devices;

transmitting, by said computer processor to said first specifiedlocation and said second mobile computing system, said second mobileusage portfolio;

generating, by said computer processor based on said first mobile usageportfolio, said second mobile usage portfolio, and said first powerconsumption data, a first load usage prediction report associated withsaid first user, said second user, and said first plurality of powerconsumption devices; and

transmitting, by said computer processor to a power provider utility foranalysis, said first load usage prediction report.

The present invention provides a power usage prediction methodcomprising:

retrieving, by a computer processor of a computing system from a firstmobile computing system, first input data associated with a first userof a first plurality of power consumption devices at a first specifiedlocation;

retrieving, by said computer processor from a second mobile computingsystem, second input data associated with a second user of a secondplurality of power consumption devices at second specified location,wherein said second specified location differs from said first specifiedlocation;

retrieving, by said computer processor from said first location, firstpower consumption data comprising a power consumption level for eachpower consumption device of said first plurality of power consumptiondevices;

retrieving, by said computer processor from said second location, secondpower consumption data comprising a power consumption level for eachpower consumption device of said second plurality of power consumptiondevices;

generating, by said computer processor based on said first input data, afirst mobile usage portfolio associated said first user and said firstplurality of power consumption devices;

transmitting, by said computer processor to said first specifiedlocation and said first mobile computing system, said first mobile usageportfolio;

generating, by said computer processor based on said second input data,a second mobile usage portfolio associated said second user and saidsecond plurality of power consumption devices;

transmitting, by said computer processor to said second specifiedlocation and said second mobile computing system, said second mobileusage portfolio;

generating, by said computer processor based on said first mobile usageportfolio, said second mobile usage portfolio, said first powerconsumption data, and said second power consumption data, a first loadusage prediction report associated with said first user, said seconduser, said first plurality of power consumption devices, and said secondplurality of power consumption devices; and

transmitting, by said computer processor to a power provider utility foranalysis, said first load usage prediction report.

The present invention advantageously provides a simple method andassociated system capable of managing usage data from a power providerfor various power consumption devices.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a system for predicting an amount of power usageassociated with a power transmission grid, in accordance withembodiments of the present invention.

FIG. 2 illustrates an alternative system to the system of FIG. 1, inaccordance with embodiments of the present invention.

FIG. 3 illustrates a flowchart describing an algorithm used by thesystem of FIG. 1 for predicting an amount of power usage associated witha power transmission grid, in accordance with embodiments of the presentinvention.

FIG. 4 illustrates a flowchart describing an algorithm used by thesystem FIG. 2 for predicting an amount of power usage associated with apower transmission grid, in accordance with embodiments of the presentinvention.

FIG. 5 illustrates a computer apparatus used for predicting an amount ofpower usage associated with a power transmission grid, in accordancewith embodiments of the present invention.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 illustrates a system 2 for predicting an amount of power usageassociated with a power transmission grid 7, in accordance withembodiments of the present invention. System 2 comprises an intelligentsystem for gathering usage profiles associated with one or more users ofone or more power consumption devices (e.g., power consumption devices14 n . . . 14 n, 24 a . . . 24 n, etc) in a residence or business. Powerusage by power consumption devices may cause a frequency signal (e.g.,60 Hertz (Hz)) associated with a supply voltage retrieved from a powergrid (e.g., power transmission grid 7) to fluctuate (e.g., rise orfall). When the frequency signal drops (i.e., indicating that a loadexceeds a supply on power transmission grid 7), a load should be reducedquickly. Therefore, system 2 is enabled to predict future powerconsumption by power consumption devices so that utility(s) may adjustpower generation accordingly. System 2 determines a future load profilefor each power consumption device and periodically transmits theassociated information to utility(s) 5 to augment a power transmissiongrid wide load profile. A load profile device (e.g., mobile computingsystem(s) 15 n), computing system 8 a, computing system 8 n, etc) may beimplemented as a software product or a hardware/software device. Theload profile device is responsible for retrieving external location,transactional, and event data. Additionally, the load profile devicetransmits updates (i.e., to a local profile) to utility(s) 5. Forexample, an air conditioner/furnace thermostat (i.e., monitoring acooling/heating schedule and determining a future time at which the airconditioner will be turned on/off) may be modified to receive calendarinformation associated with a users schedule to determine if the usersresidence will be occupied during a fractional portion of a specifiedday or an entire day. Additionally, the air conditioner/furnacethermostat may receive inputs from a security system to determine thatno motion has been detected or that an armed system has not beendeactivated. A load profile device comprises a statistical analysisengine that is coupled with additional events, sensors, and feedback togenerate better predictive value. The additional inputs may include:occupancy issues (e.g., whether or not a residence is occupied, a numberof workers present, changes in manufacturing shift schedules, etc); anexpected time when occupancy changes (e.g., residential calendar inputssuch as holidays or vacations, resumption of a manufacturing process,changes in numbers of workers during events such as monthly inventory);feedback from distributed sources of information (e.g., integration oftravel data with occupancy calendar information); and fine grainedknowledge of specific loads and scheduling (e.g., awareness ofindividual branch circuit loads such as washers and dryers combined withknowledge of laundry schedules); financial transaction information(e.g., where a credit card was swiped or where a debit transaction orphone micro payment was made); security system sensors including motiondetection units and arming status may be used to understand whetherdeviations in a normal pattern occur in order to augment a predictionalgorithm.

System 2 comprises a computing system 8 a and a computing system 8 nconnected to a utility(s) 5 through a power transmission grid 7.Computing system 8 a, computing system 8 n, and utility(s) are connectedto (e.g., via a wireless connection) and communicate with mobilecomputing system(s) 15 n. Computing system 8 a is additionally connectedto power consumption devices 14 a . . . 14 n. Computing system 8 a andpower consumption devices 14 a . . . 14 n are located within a specifiedlocation 18 a. Specified location 18 a may comprise a house andsurrounding property, a building (associated with a business) andsurrounding property, etc. Computing system 8 n and power consumptiondevices 24 a . . . 24 n are located within a specified location 18 n.Specified location 18 n may comprise a house and surrounding property, abuilding (associated with a business) and surrounding property, etc.Additional locations (similar to locations 18 a and 18 b), computingsystems (similar to computing systems 8 a and 8 b), and powerconsumption devices (similar to power consumption devices 14 a . . . 14n and 24 a . . . 24 n) may be comprised by system 2 of FIG. 1. Powerconsumption devices 14 a . . . 14 n and 24 a . . . 24 n may comprise anytype of electrical device that consumes electrical power (e.g.,appliances, a furnace, an oven, an air conditioner, a computer, a hotwater tank, an electric heater, etc) provided by utility(s) 5.Electrical power may be retrieved via a power grid (e.g., powertransmission grid 7). Utility 5 may comprise any type of electricalpower supplier that produces and/or distributes electrical power.Utilities 5 a . . . 5 n may produce and/or distribute any type ofelectrical power including, inter alia, fossil fuel generated power,steam generated power, hydro generated power, solar generated power,wind generated power, fuel cell generated power, etc. Computing system 8a and 8 n may comprise relays or contactors for enabling or disablingpower to power consumption devices 14 a . . . 14 n and 24 a . . . 24 n,respectively. Alternatively, each of power consumption devices 14 a . .. 14 n and 24 a . . . 24 n may comprise a relay or contactor thatreceives a control signal from computing system 8 a, 8 n, or mobilecomputing system(s) 15 n and in response enables or disables power topower consumption devices 14 a . . . 14 n. Computing systems 8 a and 8 nmay comprise any type of computing system including, inter alia, acomputer, a laptop/notebook computer, an embedded controller, etc.Mobile computing system(s) 15 n may comprise one or multiple mobilecomputing systems. Mobile computing system(s) 15 n may comprise any typeof mobile computing system including, inter alia, a laptop/notebookcomputer, an embedded controller in an automobile, a digital assistant(PDA), a cellular telephone, etc. Computing systems 8 a, 8 n, and mobilecomputing system(s) 15 n may each comprise a memory system. The memorysystem may comprise a single memory system. Alternatively, the memorysystem may comprise a plurality of memory systems. The memory system maybe internal or external. Computing systems 8 a, 8 n, and mobilecomputing system(s) 15 n may each comprise a software application forcontrolling functionality. Computing systems 8 a, 8 n, and mobilecomputing system(s) 15 n may communicate with utility(s) using anymethod including, inter alia, power line communication (PLC),IP-over-power, Internet, wireless, etc. A PLC comprises a system forcarrying data on a conductor used for electric power transmission.IP-over-Power comprises a system for using PLC by sending and receivingradio signals over power lines to provide access to the Internet.

Mobile computing system(s) 15 n may belong to a single user associatedwith locations 18 a and 18 n. Alternatively, mobile computing system(s)15 n may belong to multiple users associated with locations 18 a and 18n. Mobile computing system(s) 15 n are enabled to generate load profilesfor predicting an amount of power usage (i.e., with respect to powerconsumption devices 14 a . . . 14 n and 24 a . . . 24 n) associated witha power transmission grid 7. Mobile computing system(s) 15 n maytransmit (e.g., to utility(s) 5) a packet of data that includes anidentifier for the a residence or place of business, information aboutwhat type of power consumption device is being profiled (e.g., ACsystem, toaster, freezer, etc), a normal profile is for that day/time, acalculated profile is, a deviation in normal power consumption, and anexpected duration of the deviation.

The following steps illustrate a process performed by mobile computingsystem(s) 15 n for predicting an amount of power usage (i.e., withrespect to power consumption devices 14 a . . . 14 n and 24 a . . . 24n) associated with a power transmission grid 7:

1. Retrieve input data associated with a user(s) and power consumptiondevices 14 a . . . 14 n and 24 a . . . 24 n. The input data may include,inter alia, power consumption data (i.e., an amount of power consumedby) power consumption devices 14 a . . . 14 n and 24 a . . . 24 n,schedule related data associated with user schedules, location baseddata for the users, etc.2. Generate mobile usage profiles for the users and/or power consumptiondevices 14 a . . . 14 n and 24 a . . . 24 n. The mobile usage profilemay include data associated with user behavior and power consumptiondevices 14 a . . . 14 n and 24 a . . . 24 n, linkages between processesbetween users and power consumption devices 14 a . . . 14 n and 24 a . .. 24 n, etc. Linkages between processes between users and powerconsumption devices 14 a . . . 14 n and 24 a . . . 24 n may be used tobase predictions on people, device, or roles associated with users.Associations between users, devices, and processes may be generated. Forexample, in a case where process scheduling is used, a calendar thatindicates that a specified process will take place (e.g. print yearlyreports) may be used to predict a future load on power transmission grid7.3. Calculate load predictions for a given time or over a specified timeperiod. Additionally, load predictions and expected variation/deviationfrom the predictions are determined. In order to calculate loadpredictions the mobile usage profiles (generated in step 2) may betransmitted to various locations as the users move to various locations.For example, a user gets in his/her automobile and communicates thathe/she is leaving home and is going to commute to the office. Using thisinformation, mobile computing system(s) 15 n (i.e., in the automobile)notifies the users house that the user is gone and notifies the officeto predict how much power the user will consume at the office. Mobilecomputing system(s) 15 n transmits data including an expected arrivaltime and charge related data indicating if the automobile (i.e., abattery operated automobile) requires a charge, etc. Additionally,mobile computing system(s) 15 n calculates an expected load for the userat the office. Mobile computing system(s) 15 n predicts that based onschedule data that the user will require less power today because he/sheis late going into the office. In order to calculate load predictions,mobile computing system(s) 15 n may use historical data (how much powerdoes the user usually use), calendar data (allowing for accounting forload predictions for a process or device that might move from locationto location), etc4. Aggregate power usage predictions for all power consumption devices,users, and locations. The predictions may be communicated through directcommunication or masking data for transmitting through a third party. Athird party may gather user profiles from different users and locationsand provides security for the users by gathering data for multiple usersand reporting just a total or by transmitting a usage prediction for auser without identifiable information.5. A load prediction is transmitted to utilities 5.

The following implementation examples illustrate various embodimentsused by system 2 for predicting an amount of power usage associated withpower consumption devices and a power transmission grid 7.

Example 1

In this example, Widget Manufacturing (i.e., a company for manufacturingwidgets) has 100 workers who work within a central location. On atypical day, 95 workers are present and 5 workers are away on holiday orbusiness. Next Thursday the company is having a going away party for along time worker and it is determined that 25 workers will be away fromthe office. Based on this information, it is determined that the powerused by Widget Manufacturing will be 20% less than normal. Informationregarding the 20% reduction in power may be transmitted to a utility(e.g., utilities 5) providing power.

Example 2

In this example, the Smith family lives in a large, high-techneighborhood where all of the residences use a community powermanagement system (CPMS) to control heating and air conditioning (HVAC)in order to insure that a uniform load is presented to the powertransmission grid by the neighborhood. At a specified date, the Smith'shave armed their home alarm and indicated to the CPMS that they are notexpected home for 5 days. Using this information, a mobile computingsystem (e.g., mobile computing system(s) 15 n) belonging to the userrecalculates the Smith homes load profile based on minimal power usage.Additionally, weather forecasts are consulted to compute thermal loadingof the Smith home. The updated load profile is transmitted to the CPMSand the Smith home is given a lower priority for any HVAC needs. As aresult, other residents within the neighborhood will see a fasterresponse time to their HVAC needs.

Example 3

In this example, Bill Jones has configured his bank card account totransmit credit card transaction information to his home computer inreal time so that he does not have to periodically downloadtransactions. In response, a mobile computing system (e.g., mobilecomputing system(s) 15 n) uses the information to determine if Bill willbe returning home from his frequent trips ahead of schedule or not. At 4pm, a credit card transaction is received from the local airport parkingarea indicating that Bill is in town and will be home within about 2hours (i.e., a full day ahead of schedule). In response, mobilecomputing system recalculates the load profile based on Bill being athome and uploads this information to the CPMS so that his residence maybe using power for HVAC loads at a higher priority. Additionally, 2kilowatt hours of surplus solar power which were going to be soldbetween 4 pm and sundown at 7 pm are cancelled.

In order to calculate deviations associated load predictions, mobilecomputing system(s) 15 n may use the following methods:

1. Security system methods such as, inter alia, activation of motionsensors, armed/disarmed state of an alarm system, etc.

2. A Vehicle GPS tracking method may be used. For example, if twovehicles are located a greater distance than X miles from a residence orbusiness then a determination may be made that vehicle owner/driver willnot be consuming electricity at their residence for X number of hours.3. A credit card usage method may be used check for purchases made inremote places to determine that a user is at home or away from home. Forexample, if a credit card was scanned from another city it may beinferred that the user is away and won't be back home for X number ofhours.4. Calendar entries may be used to determine how many people in aresidence or business will be going into work or are away from home onany given day. The more people that are absent, the lower the powerusage will be.5. A power usage deviation method may be used. For example, if a roomlight is not turned on or a sink is not activated in X number ofminutes, it may be inferred that the house or business is not in use.6. An appliance scheduling method may be used. For example, individualmajor appliances may be scheduled for use at some time in the future.Based on past power consumption, a load at the given point in time iscalculated to include those major appliance loads.

FIG. 2 illustrates an alternative system 2 a to system 2 of FIG. 1, inaccordance with embodiments of the present invention. In contrast tosystem 2 of FIG. 1, system 2 a of FIG. 2 comprises an additionalcomputing system 15 n for predicting an amount of power usage associatedwith a power transmission grid 7. Locations 18 a and 18 b compriselocations as described with respect to location 18 of FIG. 1. Powerconsumption devices 14 a . . . 14 n and 24 a . . . 24 n comprise powerconsumption devices as described with respect to power consumptiondevices 14 a . . . 14 n of FIG. 1. Computing system 8 c, computingsystem 8 a and 8 b, and mobile computing system(s) 15 n each comprise acomputing system as described with respect to FIG. 1. Computing system 8c retrieves data from mobile computing systems (i.e., associated withmultiple users and locations) and uses the retrieved data for predictingan amount of power usage associated with a power transmission grid 7.Computing system 8 c may comprise a third party (i.e., not associatedwith utility(s), users, or locations 18 a and 18 n) computing system forproviding secure power usage predictions.

FIG. 3 illustrates a flowchart describing an algorithm used by system 2of FIG. 1 for generating load usage prediction reports for a singleuser, in accordance with embodiments of the present invention. In step302, a computing system (e.g., a computer processor of mobile computingsystem(s) 15 n of FIG. 1 retrieves input data associated with a user ofa power consumption devices (e.g., power consumption devices 14 a . . .14 n) at a specified location. In step 304, the computing systemretrieves power consumption data comprising a power consumption levelfor each of the power consumption devices. In step 308, the computingsystem generates (i.e., based on the input data, a mobile usageportfolio associated the first user and the power consumption devices.In step 312, the computing system transmits (to the specified location)the mobile usage portfolio. In step 318, the computing system generates(i.e., based on the mobile usage portfolio and the power consumptiondata) a load usage prediction report associated with the user and thepower consumption devices. The load usage prediction report maycomprise, inter alia, a predicted amount of power usage for the powerconsumption devices during a future specified time period, an expectedamount of variation from the predicted amount of power usage, a currentlocation of the user, a distance between locations, etc. In step 324,the computing system transmits the load usage prediction report to apower provider utility for analysis and the process is repeated for thesame user at another location, a generating a modified load usageprediction report using new data, etc.

FIG. 4 illustrates a flowchart describing an algorithm used by system 2a of FIG. 1 for generating load usage prediction reports for multipleusers, in accordance with embodiments of the present invention. In step302, a computing system (e.g., a computer processor computing system 8 cof FIG. 2 retrieves input data (from mobile computing systems)associated with:

1. A first user of first power consumption devices (e.g., powerconsumption devices 4 a . . . 14 n) at a first specified location (e.g.,location 18 a).

2. A second user of first power consumption devices (e.g., powerconsumption devices 14 a . . . 14 n) at a first specified location(e.g., location 18 a) or second power consumption devices (e.g., powerconsumption devices 24 a . . . 24 n) at a second specified location(e.g., location 18 n).

In step 404, the computing system retrieves power consumption datacomprising a power consumption level for each of the power consumptiondevices. In step 308, the computing system retrieves power consumptiondata comprising a power consumption level for each of the first and/orsecond power consumption devices. In step 408, the computing systemgenerates (i.e., based on the input data), mobile usage portfoliosassociated the first and second users and the first and/or second powerconsumption devices. In step 412, the computing system transmits (to thespecified locations) the associated mobile usage portfolios. In step418, the computing system generates (i.e., based on the mobile usageportfolios and the power consumption data) an aggregated load usageprediction report associated with the first and second users and thepower consumption devices. The load usage prediction report maycomprise, inter alia, a predicted amount of power usage for the powerconsumption devices during a future specified time period, an expectedamount of variation from the predicted amount of power usage, a currentlocation of the user, a distance between locations, etc. In step 424,the computing system transmits the aggregated load usage predictionreport to a power provider utility for analysis and the process isrepeated for the first and second users (add optionally additionalusers) at the same or different locations, generating a modified loadusage prediction report using new data, etc. retrieves and storesidentification data identifying a power consumption devices at a singlelocation (e.g., locally at the single location) or multiple locations(e.g., at a regional

FIG. 5 illustrates a computer apparatus 90 (e.g., computing system 8 a,8 n, 8 c, or 15 n of FIGS. 1 and 2) used for predicting an amount ofpower usage associated with a power transmission grid, in accordancewith embodiments of the present invention. The computer system 90comprises a processor 91, an input device 92 coupled to the processor91, an output device 93 coupled to the processor 91, and memory devices94 and 95 each coupled to the processor 91. The input device 92 may be,inter alia, a keyboard, a mouse, etc. The output device 93 may be, interalia, a printer, a plotter, a computer screen, a magnetic tape, aremovable hard disk, a floppy disk, etc. The memory devices 94 and 95may be, inter alia, a hard disk, a floppy disk, a magnetic tape, anoptical storage such as a compact disc (CD) or a digital video disc(DVD), a dynamic random access memory (DRAM), a read-only memory (ROM),etc. The memory device 95 includes a computer code 97. The computer code97 includes algorithms (e.g., the algorithms of FIGS. 3-4) forpredicting an amount of power usage associated with a power transmissiongrid. The processor 91 enables the computer code 97. The memory device94 includes input data 96. The input data 96 includes input required bythe computer code 97. The output device 93 displays output from thecomputer code 97. Either or both memory devices 94 and 95 (or one ormore additional memory devices not shown in FIG. 5) may comprise thealgorithms of FIGS. 3-4 and may be used as a computer usable medium (ora computer readable medium or a program storage device) having acomputer readable program code embodied therein and/or having other datastored therein, wherein the computer readable program code comprises thecomputer code 97. Generally, a computer program product (or,alternatively, an article of manufacture) of the computer system 90 maycomprise said computer usable medium (or said program storage device).

Still yet, any of the components of the present invention could becreated, integrated, hosted, maintained, deployed, managed, serviced,etc. by a service supplier who offers to predict an amount of powerusage associated with a power transmission grid. Thus the presentinvention discloses a process for deploying, creating, integrating,hosting, maintaining, and/or integrating computing infrastructure,comprising integrating computer-readable code into the computer system90, wherein the code in combination with the computer system 90 iscapable of performing a method for predicting an amount of power usageassociated with a power transmission grid. In another embodiment, theinvention provides a business method that performs the process steps ofthe invention on a subscription, advertising, and/or fee basis. That is,a service supplier, such as a Solution Integrator, could offer topredict an amount of power usage associated with a power transmissiongrid. In this case, the service supplier can create, maintain, support,etc. a computer infrastructure that performs the process steps of theinvention for one or more customers. In return, the service supplier canreceive payment from the customer(s) under a subscription and/or feeagreement and/or the service supplier can receive payment from the saleof advertising content to one or more third parties.

While FIG. 5 shows the computer system 90 as a particular configurationof hardware and software, any configuration of hardware and software, aswould be known to a person of ordinary skill in the art, may be utilizedfor the purposes stated supra in conjunction with the particularcomputer system 90 of FIG. 5. For example, the memory devices 94 and 95may be portions of a single memory device rather than separate memorydevices.

While embodiments of the present invention have been described hereinfor purposes of illustration, many modifications and changes will becomeapparent to those skilled in the art. Accordingly, the appended claimsare intended to encompass all such modifications and changes as fallwithin the true spirit and scope of this invention.

What is claimed is:
 1. A power usage prediction method comprising:retrieving, by a computer processor of a mobile computing system, firstinput data associated with a first user of a first plurality of powerconsumption devices at a first specified location, wherein said firstinput data comprises location data indicating a current location of saidfirst user, and wherein said current location differs from said firstspecified location; retrieving, by said computer processor, first powerconsumption data comprising a power consumption level for each powerconsumption device of said first plurality of power consumption devices;generating, by said computer processor based on said first input data, afirst mobile usage portfolio associated said first user and said firstplurality of power consumption devices; transmitting, by said computerprocessor to said first specified location, said first mobile usageportfolio; monitoring, by said computer processor, usage of plumbingfixtures at said first specified location; determining, by said computerprocessor based on results of said monitoring, a plumbing usagedeviation value indicating that at least one plumbing fixture of saidplumbing fixtures has not been used for a specified time period;generating, by said computer processor based on said first mobile usageportfolio, said plumbing usage deviation value, and said first powerconsumption data, a first load usage prediction report associated withsaid first user and said first plurality of power consumption devices;and transmitting, by said computer processor to a power provider utilityfor analysis, said first load usage prediction report.
 2. The method ofclaim 1, further comprising: transmitting, by said computer processor tosaid first specified location, said first load usage prediction report.3. The method of claim 1, further comprising: retrieving, by saidcomputer processor, second input data associated with said first user,wherein said second input data is associated with a second plurality ofpower consumption devices used by said first user at a second specifiedlocation, wherein said second specified location differs from said firstspecified location; retrieving, by said computer processor, second powerconsumption data comprising a power consumption level for each powerconsumption device of said second plurality of power consumptiondevices; generating, by said computer processor based on said secondinput data and said first mobile usage portfolio, a second mobile usageportfolio associated said first user and said second plurality of powerconsumption devices; transmitting, by said computer processor to saidsecond specified location, said second mobile usage portfolio;generating, by said computer processor based on said first mobile usageportfolio, said second mobile usage portfolio, and said second powerconsumption data, a second load usage prediction report associated withsaid first user, said first plurality of power consumption devices, andsaid second plurality of power consumption devices; and transmitting, bysaid computer processor to said power provider utility for analysis,said second load usage prediction report.
 4. The method of claim 1,further comprising: retrieving, by said computer processor, updatedinput data associated with said first user of said first plurality ofpower consumption devices at said first specified location; generating,by said computer processor based on said updated input data and saidfirst mobile usage portfolio, a modified mobile usage portfolioassociated said first user and said first plurality of power consumptiondevices; transmitting, by said computer processor to said firstspecified location, said modified mobile usage portfolio; generating, bysaid computer processor based on said modified mobile usage portfolio, amodified load usage prediction report associated with said first userand said first plurality of power consumption devices; and transmitting,by said computer processor to said power provider utility for analysis,said modified load usage prediction report.
 5. The method of claim 1,wherein said generating said first load usage prediction reportcomprises: calculating, by said computer processor, a predicted amountof power usage for said first plurality of power consumption devicesduring a future specified time period; and calculating, by said computerprocessor, an expected amount of variation from said predicted amount ofpower usage.
 6. The method of claim 1, wherein said first mobile usageportfolio comprises behavioral data associated with said first user andsaid first plurality of power consumption devices.
 7. The method ofclaim 1, wherein said generating said first mobile usage portfoliocomprises: retrieving, by said computer processor, a process scheduleassociated with processes performed by said first plurality of powerconsumption devices; and generating associations between said firstuser, said first plurality of power consumption devices, and saidprocess schedule.
 8. The method of claim 1, wherein said first inputdata comprises digital calendar data indicating a future schedule ofsaid first user with respect to said first specified location.
 9. Acomputer program product, comprising a computer hardware storage devicestoring a computer readable program code, said computer readable programcode configured to perform the method of claim 8 upon being executed bysaid computer processor of said computing system.
 10. The method ofclaim 1, further comprising: detecting, by said computer processor, saidcurrent location of said first user; determining, by said computerprocessor, a distance between said current location and said firstspecified location; generating, by said computer processor, updatedinput data associated with said first user and said distance betweensaid current location and said first specified location; generating, bysaid computer processor based on said updated input data and said firstmobile usage portfolio, a modified mobile usage portfolio associatedsaid first user and said first plurality of power consumption devices;transmitting, by said computer processor to said first specifiedlocation, said modified mobile usage portfolio; generating, by saidcomputer processor based on said modified mobile usage portfolio, amodified load usage prediction report associated with said first userand said first plurality of power consumption devices; and transmitting,by said computer processor to said power provider utility for analysis,said modified load usage prediction report.
 11. The method of claim 10,wherein said detecting comprises communicating with a satellite todetermine said current location.
 12. A process for supporting computerinfrastructure, said process comprising providing at least one supportservice for at least one of creating, integrating, hosting, maintaining,and deploying computer-readable code, stored on a computer hardwarestorage device, in said computer processor, wherein the code incombination with the computer processor is capable of performing themethod of claim
 1. 13. A computing system comprising said processorcoupled to a computer-readable memory unit, said memory unit comprisinga computer readable code configured to be enabled by the processor toperform the method of claim
 1. 14. The method of claim 1, furthercomprising: monitoring, by said computer processor, motion detectors atsaid first specified location; and detecting, by said computer processorbased on results of said monitoring said motion detectors, motion ofindividuals at said first specified location, wherein said generatingsaid first load usage prediction report is further based on results ofsaid detecting.
 15. The method of claim 1, further comprising:receiving, by said computer processor, data indicating an expectednumber of individuals expected at said first specified location, whereinsaid generating said first load usage prediction report is further basedon said data.
 16. The method of claim 1, further comprising: monitoring,by said computer processor, financial transaction data associated withfinancial transactions of said first user; and determining, by saidcomputer processor based on results of said monitoring said financialtransactions, a distance between said current location and said firstspecified location, wherein said generating said first load usageprediction report is further based on said distance.
 17. A power usageprediction method comprising: retrieving, by a computer processor of acomputing system from a first mobile computing system, first input dataassociated with a first user of a first plurality of power consumptiondevices at a first specified location, wherein said first input datacomprises location data indicating a current location of said firstuser, and wherein said current location differs from said firstspecified location; retrieving, by said computer processor from a secondmobile computing system, second input data associated with a second userof said first plurality of power consumption devices at said firstspecified location; retrieving, by said computer processor from saidfirst location, first power consumption data comprising a powerconsumption level for each power consumption device of said firstplurality of power consumption devices; generating, by said computerprocessor based on said first input data, a first mobile usage portfolioassociated said first user and said first plurality of power consumptiondevices; transmitting, by said computer processor to said firstspecified location and said first mobile computing system, said firstmobile usage portfolio; monitoring, by said computer processor, usage ofplumbing fixtures at said first specified location; determining, by saidcomputer processor based on results of said monitoring, a plumbing usagedeviation value indicating that at least one plumbing fixture of saidplumbing fixtures has not been used for a specified time period;generating, by said computer processor based on said second input data,a second mobile usage portfolio associated said second user and saidfirst plurality of power consumption devices; transmitting, by saidcomputer processor to said first specified location and said secondmobile computing system, said second mobile usage portfolio; generating,by said computer processor based on said first mobile usage portfolio,said second mobile usage portfolio, said plumbing usage deviation value,and said first power consumption data, a first load usage predictionreport associated with said first user, said second user, and said firstplurality of power consumption devices; and transmitting, by saidcomputer processor to a power provider utility for analysis, said firstload usage prediction report.
 18. The method of claim 17, furthercomprising: detecting, by said computer processor, a first currentlocation of said first user, wherein said first current location differsfrom said first specified location; detecting, by said computerprocessor, a second current location of said second user, wherein saidsecond current location differs from said first current location andsaid first specified location; determining, by said computer processor,a first distance between said first current location and said firstspecified location; determining, by said computer processor, a seconddistance between said second current location and said first specifiedlocation, wherein said first distance differs from said second distance;generating, by said computer processor, first updated input dataassociated with said first user and said first distance between saidfirst current location and said first specified location; generating, bysaid computer processor, second updated input data associated with saidsecond user and said second distance between said second currentlocation and said first specified location; generating, by said computerprocessor based on said first updated input data and said first mobileusage portfolio, a first modified mobile usage portfolio associated saidfirst user and said first plurality of power consumption devices;transmitting, by said computer processor to said first specifiedlocation and said first mobile computing system, said first modifiedmobile usage portfolio; generating, by said computer processor based onsaid second updated input data and said second mobile usage portfolio, asecond modified mobile usage portfolio associated said second user andsaid first plurality of power consumption devices; transmitting, by saidcomputer processor to said first specified location and said secondmobile computing system, said second modified mobile usage portfolio;generating, by said computer processor based on said first updatedmobile usage portfolio, said second updated mobile usage portfolio, andsaid first power consumption data, a modified load usage predictionreport associated with said first user, said second user, and said firstplurality of power consumption devices; and transmitting, by saidcomputer processor to said power provider utility for analysis, saidmodified load usage prediction report.
 19. A power usage predictionmethod comprising: retrieving, by a computer processor of a computingsystem from a first mobile computing system, first input data associatedwith a first user of a first plurality of power consumption devices at afirst specified location, wherein said first input data compriseslocation data indicating a current location of said first user, andwherein said current location differs from said first specifiedlocation; retrieving, by said computer processor from a second mobilecomputing system, second input data associated with a second user of asecond plurality of power consumption devices at second specifiedlocation, wherein said second specified location differs from said firstspecified location; retrieving, by said computer processor from saidfirst location, first power consumption data comprising a powerconsumption level for each power consumption device of said firstplurality of power consumption devices; retrieving, by said computerprocessor from said second location, second power consumption datacomprising a power consumption level for each power consumption deviceof said second plurality of power consumption devices; generating, bysaid computer processor based on said first input data, a first mobileusage portfolio associated said first user and said first plurality ofpower consumption devices; transmitting, by said computer processor tosaid first specified location and said first mobile computing system,said first mobile usage portfolio; monitoring, by said computerprocessor, usage of plumbing fixtures at said first specified location;determining, by said computer processor based on results of saidmonitoring, a plumbing usage deviation value indicating that at leastone plumbing fixture of said plumbing fixtures has not been used for aspecified time period; generating, by said computer processor based onsaid second input data, a second mobile usage portfolio associated saidsecond user and said second plurality of power consumption devices;transmitting, by said computer processor to said second specifiedlocation and said second mobile computing system, said second mobileusage portfolio; generating, by said computer processor based on saidfirst mobile usage portfolio, said second mobile usage portfolio, saidfirst power consumption data, said plumbing usage deviation value, andsaid second power consumption data, a first load usage prediction reportassociated with said first user, said second user, said first pluralityof power consumption devices, and said second plurality of powerconsumption devices; and transmitting, by said computer processor to apower provider utility for analysis, said first load usage predictionreport.
 20. The method of claim 19, further comprising: detecting, bysaid computer processor, a first current location of said first user,wherein said first current location differs from said first specifiedlocation; detecting, by said computer processor, a second currentlocation of said second user, wherein said second current locationdiffers from said first current location, said first specified location,and said second specified location; determining, by said computerprocessor, a first distance between said first current location and saidfirst specified location; determining, by said computer processor, asecond distance between said second current location and said secondspecified location, wherein said first distance differs from said seconddistance; generating, by said computer processor, first updated inputdata associated with said first user and said first distance betweensaid first current location and said first specified location;generating, by said computer processor, second updated input dataassociated with said second user and said second distance between saidsecond current location and said second specified location; generating,by said computer processor based on said first updated input data andsaid first mobile usage portfolio, a first modified mobile usageportfolio associated said first user and said first plurality of powerconsumption devices; transmitting, by said computer processor to saidfirst specified location and said first mobile computing system, saidfirst updated mobile usage portfolio; generating, by said computerprocessor based on said second updated input data and said second mobileusage portfolio, a second modified mobile usage portfolio associatedsaid second user and said second plurality of power consumption devices;transmitting, by said computer processor to said second specifiedlocation and said second mobile computing system, said second updatedmobile usage portfolio; generating, by said computer processor based onsaid first updated mobile usage portfolio, said second updated mobileusage portfolio, said first power consumption data, and said secondpower consumption data, a modified load usage prediction reportassociated with said first user, said second user, said first pluralityof power consumption devices, and said second plurality of powerconsumption devices; and transmitting, by said computer processor tosaid power provider utility for analysis, said modified load usageprediction report.